Abstract:
The rapid development of industry enterprises,
the large amount of data generated by these
originalities and the exponential growth of industrial
business website are the causes that lead to different
types of big data and data stream problem. There are
many stream data mining algorithms for classification
and clustering with their specific properties and
significance key features. Ensemble classifiers help to
improve the best predictive performance results
among these up-to-date algorithms. In ensemble
methods, different kinds of classifiers and clusters are
trained rather than training single classifier. Their
prediction machine learning results are combined to a
voting schedule. This paper presented a framework for
stream data mining by taking the benefits of
assembling technology based on miss classification
stream data. Experiments are carried out with real
world data streams. The experimental performance
results are compared with the modern popular
ensemble techniques such as Boosting and Bagging.
The increasing in accuracy rate and the reducing in
classification time can be seen from the test results.
